Abstract: A method for integrated professional network expansion during employee onboarding is implemented via a computing system including a processor. The method includes executing, via a network, an enterprise application on a first remote computing system operated by an employer associated with an enterprise, as well as causing surfacing of a professional networking UI on a display of the first remote computing system during the execution of the enterprise application. The method also includes receiving, via the professional networking UI surfaced on the display of the first remote computing system, user input including a specification of onboarding parameters for an employee of the enterprise, where the onboarding parameters correspond to one or more professional networking intentions for the employee. The method further includes causing the enterprise application to be executed on a second remote computing system operated by the employee in accordance with the specified onboarding parameters.

Abstract: A method for friction reduction during professional network expansion is implemented via a computing system including a processor. The method includes executing, via a network, an enterprise application on a remote computing system operated by a user associated with an enterprise and surfacing a professional networking UI on a display of the remote computing system during execution of the enterprise application. The method includes generating parameters that are representative of a connection between the user and each suggested professional contact based on enterprise-level data corresponding to the user and each suggested professional contact and generating friction-reducing UI elements for each suggested professional contact based on the generated parameters.

Abstract: A method for data-driven professional network expansion techniques is implemented via a computing system including a processor. The method includes executing, via a network, an enterprise application on a remote computing system operated by a user associated with an enterprise and surfacing a professional networking UI on a display of the remote computing system during execution of the enterprise application. The method includes accessing enterprise-level data corresponding to the user and the enterprise, extracting parameters associated with the user and the enterprise from the enterprise-level data, and automatically generating, based on the extracted parameters, the user's professional network and a list of suggested professional contacts.

Abstract: A system and method for identifying a user is described. The system identifies collaboration metrics based on user interaction data of users of an application from an enterprise. The system accesses enterprise organizational data of the enterprise and identifies topic data from the user interaction data and the enterprise organizational data. The system trains a machine learning model based on the collaboration metrics, the enterprise organizational data, and the topic data.

Abstract: A system and method for identifying a user is described. The system identifies collaboration metrics based on user interaction data of users of an application from an enterprise. The system accesses enterprise organizational data of the enterprise and identifies topic data from the user interaction data and the enterprise organizational data. The system trains a machine learning model based on the collaboration metrics, the enterprise organizational data, and the topic data.

Abstract: A system and method for facilitating convergence includes receiving a request to schedule a meeting at a meeting time, retrieve at least one of user data, contextual data, facility data and map data, the user data including a list of a plurality of meeting participants for the meeting, providing at least one of the list of the plurality of meeting participants, the meeting time, the user data, and the facility data to a trained machine-learning (ML) model for predicting a location at which two or more of the plurality of meeting participants will be located within a given time period prior to the meeting time, receiving the predicted location as an output from the trained ML model, and identifying a meeting location from among one or more meeting venues based on the predicted location of the two or more of the plurality of meeting participants, and providing the meeting location for display in a first selectable user interface element.

Abstract: A system and method for facilitating convergence includes receiving data relating to a first user's history of connections with other users, receiving contextual data about the first user, the contextual data including organizational graph data, providing the data and the contextual data as inputs to a trained machine-learning (ML) model for identifying a second user for the first user to connect with based on the data and the contextual data, receiving the identified second user as an output from the trained ML model, and providing the identified second user as a connection suggestion for display in a user interface element.

Abstract: A system and method for managing one or more physical spaces includes receiving data relating to location of a plurality of users, determining based on the location, an occupancy rate of the plurality of users for at least one of the one or more physical spaces, identifying based on the occupancy rate and features provided at the one or more physical spaces, one or more user preferred features, providing the occupancy rate and the user preferred features to a trained machine-learning (ML) model for determining optimal uses for the one or more physical spaces in a future time period, receiving as an output from the trained ML model suggested plans for use or management of the one or more physical spaces in the future time period, and providing the suggested plans for display in a user interface (UI) screen.

Abstract: Techniques for interaction based social distance quantification are discloses herein. In one example, a method includes generating an interaction graph having (i) multiple vertices individually corresponding to multiple users and (ii) multiple directional edges individually connecting pairs of the multiple vertices. The method can also include applying graph embedding to convert the multiple vertices in the generated graph into corresponding tensors in a vector space. The method can further includes determining and outputting a social distance value between the one of the multiple users and another of the multiple users by calculating a tensor distance in the vector space between tensors corresponding to the one of the multiple users and the another of the multiple users.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for enhancing remote work productivity data. In one embodiment, worker data is obtained for a set of target workers and a set of control workers. The target workers are generally identified as working in the office prior to a treatment date and working remote after a treatment date, and the control workers are generally identified as working remote before and after the treatment date. Thereafter, an effect of working remotely is determined using a difference-in-differences model that measures differences in observational changes between the set of target workers and the set of control workers measured before and after the treatment date. The effect of working remotely can be used to generate a generating a productivity score indicating an extent of productivity in relation to the set of target workers.

Abstract: A system and method for configuring an application is described. The system identifies user collaboration data of users of an application. The user collaboration data indicates durations of collaborations between one or more users using the application. The system computes an influencer score of a first user based on the duration of collaborations between the first user and the other users. The system determines that the influencer score of the first user exceeds an influencer score threshold and generates a configuration setting of the application for the first user. The system applies the configuration setting to the application for the first user.

Abstract: A system and method for identifying a user is described. The system identifies collaboration metrics based on user interaction data of users of an application from an enterprise. The system accesses enterprise organizational data of the enterprise and identifies topic data from the user interaction data and the enterprise organizational data. The system trains a machine learning model based on the collaboration metrics, the enterprise organizational data, and the topic data.